mlsanityData to 14 Oct 2026

Autonomous overtaking trajectory optimization using reinforcement learning and opponent pose estimation

arXiv 2603.27207 ↗First posted 28 Mar 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Vehicle overtaking is one of the most complex driving maneuvers for autonomous vehicles. To achieve optimal autonomous overtaking, driving systems rely on multiple sensors that enable safe trajectory optimization and overtaking efficiency. This paper presents a reinforcement learning mechanism for multi-agent autonomous racing environments, enabling overtaking trajectory optimization, based on LiDAR and depth image data. The developed reinforcement learning agent uses pre-generated raceline data and sensor inputs to compute the steering angle and linear velocity for optimal overtaking. The system uses LiDAR with a 2D detection algorithm and a depth camera with YOLO-based object detection to identify the vehicle to be overtaken and its pose. The LiDAR and the depth camera detection data are fused using a UKF for improved opponent pose estimation and trajectory optimization for overtaking in racing scenarios. The results show that the proposed algorithm successfully performs overtaking maneuvers in both simulation and real-world experiments, with pose estimation RMSE of (0.0816, 0.0531) m in (x, y).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 17 references that are in the map. First posted on arXiv 28 Mar 2026.

Builds on

6 of 17 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  3. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 64
  4. 2020Learning from Simulation, Racing in Realitycited by 16
  5. 2023High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learningcited by 10
  6. 2022TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approachcited by 8

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2021Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
  2. 2025Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods
  3. 2023Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning
  4. 2024Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios
  5. 2025Vision based driving agent for race car simulation environments
  6. 2026Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing
  7. 2026Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
  8. 2026Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning